Recurring ventricular arrhythmias and sudden cardiac death are common consequences of coronary artery disease and other advanced forms of heart disease. Initial results with implanted devices capable of terminating ventricular tachycardia or converting ventricular fibrillation have been encouraging in that sudden deaths have been reduced from expected levels of 20-30% annually to under 2% in some series. Unfortunately the lack of specificity with which these devices detect "abnormal tachycardias" has resulted in a high incidence of spurious, painful, and potentially dangerous shocks. If such devices are to reach their potential a new approach to automated rhythm identification is necessary. In previous studies we found that, in both experimental animals and man, endocardial electrogram morphology, recorded from clinically used pacing electrodes, changes dramatically when the cardiac rhythm changes from sinus to ventricular or from one ventricular rhythm to another if their activation sequences are different. We have also shown that other factors which effect cardiac rate and function such as changes in heart rate, the administration of cardioactive drugs, and tissue growth around the electrode do not materially interfere with rhythm identification using morphologic criteria. We are currently examining electrograms from implanted defibrillation electrodes during similar interventions. The major premise to be tested in the studies proposed in this application is that the morphology of electrograms recorded from clinically used electrode systems is determined by the pattern of activation of the ventricles as a result of the specific contributions of areas of activation occurring at some distance from the electrode. In a series of mapping experiments we will evaluate several anatomic and electrophysiologic factors which may be instrumental in determining the relationship between activation sequence and electrogram morphology. In addition, we propose to design and construct a prototype system for testing, in man, specific algorithm for rhythm identification.